Improved forecasting of the compressive strength of ultra-high-performance concrete (UHPC) via the CatBoost model optimized with different algorithms

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dc.authoridErgen, Faruk/0000-0002-1509-8720
dc.authoridKatlav, Metin/0000-0001-9093-7195
dc.authorwosidErgen, Faruk/JUF-1244-2023
dc.authorwosidKatlav, Metin/HSF-7829-2023
dc.contributor.authorKatlav, Metin
dc.contributor.authorErgen, Faruk
dc.date.accessioned2024-08-04T20:56:03Z
dc.date.available2024-08-04T20:56:03Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis paper focuses on the applicability of CatBoost models constructed using various optimization techniques for improved forecasting the compressive strength of ultra-high-performance concrete (UHPC). Phasor particle swarm optimization (PPSO), dwarf mongoose optimization (DMO), and atom search optimization (ASO), which have been very popular recently, are preferred as optimization algorithms. A comprehensive and reliable data set is used to develop the CatBoost models, which include 785 test results with 15 input features. The performance of the CatBoost models (PPSO-CatBoost, DMO-CatBoost, and ASO-CatBoost) optimized with different algorithms is thoroughly assessed by means of various statistical metrics and error analysis to determine the model with the best forecasting capability, and this model is compared with the models obtained from previous studies. In addition, Shapley additive exPlanations (SHAP) analysis is used to ensure the interpretability of the forecasting models and to overcome the black box problem of machine learning (ML) models. The obtained results demonstrate that all CatBoost models outstandingly forecast the compressive strength of UHPC. Among these models, the DMO-CatBoost model stands out compared to the other models in various performance metrics, such as high coefficient of determination (R2) values, low root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) values, along with a smaller error ratio. In other words, the RMSE, R-2, MAPE, and MAE values of the DMO-CatBoost model for the training set are 3.67, 0.993, 0.019, and 2.35, respectively, whereas those for the test set are 6.15, 0.978, 0.038, and 4.51. Additionally, the performance ranking of the algorithms used to optimize the hyperparameters of the CatBoost model is as follows: DMO > PPSO > ASO. On the other hand, SHAP analysis showed that age, fiber dosage, and cement dosage significantly influence the compressive strength of UHPC. These findings can guide structural engineers in the design and optimization of UHPC, thus assisting them in developing strategies to improve the strength properties of the material. Finally, based on the best forecasting model developed in this work, a graphical user interface has been developed to easily forecast the compressive strength of UHPC in practical applications without additional tools or software.en_US
dc.identifier.doi10.1002/suco.202400163
dc.identifier.issn1464-4177
dc.identifier.issn1751-7648
dc.identifier.scopus2-s2.0-85193610782en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1002/suco.202400163
dc.identifier.urihttps://hdl.handle.net/11616/102003
dc.identifier.wosWOS:001226549700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherErnst & Sohnen_US
dc.relation.ispartofStructural Concreteen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCatBoosten_US
dc.subjectcompressive strengthen_US
dc.subjectdwarf mongoose optimizationen_US
dc.subjectoptimization techniquesen_US
dc.subjectultra-high-performance concreteen_US
dc.titleImproved forecasting of the compressive strength of ultra-high-performance concrete (UHPC) via the CatBoost model optimized with different algorithmsen_US
dc.typeArticleen_US

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